Study Area

I collect data at Fort Campbell, KY. Fort Campbell is in KY, but it also part of it is also TN. Fort Campbell is a military installation where there is approximately 105,000 acres of land with varying environments that include forests consisting of primarily deciduous trees, grasslands, wetlands, and agricultural fields. There are approximately 453 miles of streams scattered throughout the base.

Google Earth Engine

Let’s import my static maps created from Google Earth Engine!

tmax <- rast("tmax.tif")

prcp <- rast("prcp.tif")

vp <- rast("vp.tif")

# load in fc data
mil <- st_read("tl_2022_us_mil.shp")
## Reading layer `tl_2022_us_mil' from data source 
##   `C:\Users\gtomboc\OneDrive - Austin Peay State University\masters\data_analytics\basics_mapping\tl_2022_us_mil.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 858 features and 8 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -159.7881 ymin: 13.30706 xmax: 174.156 ymax: 64.87795
## Geodetic CRS:  NAD83
fc <- mil[mil$FULLNAME == "Ft Campbell" | grepl("Campbell", mil$FULLNAME), ]

fc <- st_transform(fc, crs = "EPSG:4326")

fc_vect <- vect(fc)

tmax_fc <- mask(crop(tmax, fc_vect), fc_vect)
tmax_fc <- mask(tmax_fc, fc_vect, inverse = FALSE)

prcp_fc <- mask(crop(prcp, fc_vect), fc_vect)
prcp_fc <- mask(prcp_fc, fc_vect, inverse = FALSE)

vp_fc <- mask(crop(vp, fc_vect), fc_vect)
vp_fc <- mask(vp_fc, fc_vect, inverse = FALSE)

#ggplots

tmax_df <- as.data.frame(tmax_fc, xy = TRUE, na.rm = TRUE)
prcp_df <- as.data.frame(prcp_fc, xy = TRUE, na.rm = TRUE)
vp_df <- as.data.frame(vp_fc, xy = TRUE, na.rm = TRUE)

This is a static map that shows the maximum temperature from July 1, 2024 - July 9, 2024. The highest average temperature throughout this week is taking place right outside of the urbanization of Fort Campbell.

#max temp static map
ggplot() + 
  geom_raster(data = tmax_df, aes(x = x, y = y, fill = tmax), interpolate = FALSE) +
  geom_sf(data = fc, fill = NA, color = "black", linewidth = 1) +
  coord_sf(xlim = c(st_bbox(fc)["xmin"], st_bbox(fc)["xmax"]),
           ylim = c(st_bbox(fc)["ymin"], st_bbox(fc)["ymax"]),
           expand = FALSE) +
  scale_fill_gradientn(colors = hcl.colors(100, "RdYlBu", rev = TRUE),
                      name = "°C") +
  labs(title = "Maximum Temperature (July 2024)",
  x = "Longitude", y = "Latitude") +
  theme(
  panel.background = element_blank(),
  panel.grid = element_blank(),
  panel.border = element_rect(color = "black",
  fill = NA, size = 1))

This is a static map of the average precipitation through out that week. The most precipitation occurred in the North West side of Fort Campbell.

#precipitation static map
ggplot() + 
  geom_raster(data = prcp_df, aes(x = x, y = y, fill = prcp), interpolate = FALSE) +
  geom_sf(data = fc, fill = NA, color = "black", linewidth = 1) +
  coord_sf(xlim = c(st_bbox(fc)["xmin"], st_bbox(fc)["xmax"]),
           ylim = c(st_bbox(fc)["ymin"], st_bbox(fc)["ymax"]),
           expand = FALSE) +
  scale_fill_gradientn(colors = hcl.colors(100, "Blues", rev = TRUE)) +
  labs(title = "Precipitation (July 2024)",
  x = "Longitude", y = "Latitude") +
  theme(
  panel.background = element_blank(),
  panel.grid = element_blank(),
  panel.border = element_rect(color = "black",
  fill = NA, size = 1))

This is a static map of vapor pressure through out the week. As you can see, there is a better distribution of vapor pressure throughout Fort Campbell itself.

#vapor pressure static map
ggplot() + 
  geom_raster(data = vp_df, aes(x = x, y = y, fill = vp), interpolate = FALSE) +
  geom_sf(data = fc, fill = NA, color = "black", linewidth = 1) +
  coord_sf(xlim = c(st_bbox(fc)["xmin"], st_bbox(fc)["xmax"]),
           ylim = c(st_bbox(fc)["ymin"], st_bbox(fc)["ymax"]),
           expand = FALSE) +
  scale_fill_gradientn(colors = hcl.colors(100, "YlOrRd", rev = TRUE)) +
  labs(title = "Vapor Pressure (July 2024)",
  x = "Longitude", y = "Latitude") +
  theme(
  panel.background = element_blank(),
  panel.grid = element_blank(),
  panel.border = element_rect(color = "black",
  fill = NA, size = 1))

Let’s look at a map of Kentucky and Tennessee to get an idea of my study area!

#Create map
ggplot() +
  #states surrounding
  geom_polygon(
  data = state %>% filter(region %in% border_states),
  aes(x = long, y = lat, group = group),
  fill = "snow2", color = "black") +
  #ky + tn with ALL counties
  geom_polygon(data = ky_tn,
  aes(x = long, y = lat, group = group),
  fill = "azure", color = "black") +
  #highlight the counties i use
  geom_polygon(data = counties,
               aes(x = long, y = lat, group = group),
               fill = "darkgrey", color = "black") +
  #put fort campbell (palmyra rd as a point)
  geom_point(data = fort_campbell,
              aes(x = x, y = y),
              color = "red",
              size = 2,
              stroke = 1) +
  coord_fixed(xlim = c(-90, -82), ylim = c(33.5, 39)) +
  scale_x_continuous(breaks = seq(-91, -83, by = 1)) +
  scale_y_continuous(breaks = seq(34, 39, by = 1)) +
    xlab("Longitude") + ylab("Latitude") + ggtitle("Fort Campbell Study Area") +
  theme_classic() +
    theme(
    panel.grid = element_blank()
    ) +
annotation_scale(
  location = "tl",
  width_hint = 0.3,
  style = "ticks",
  text_cex = 0.8
) +
annotation_north_arrow(
  location = "bl",
  which_north = "true",
  style = north_arrow_nautical(),
  height = unit(0.8, "cm"),
  width = unit(0.8, "cm")
)
## Using plotunit = 'm'
## Warning in draw_panel(...): True north is not meaningful without coord_sf()

Now, let’s view the sites where I caught red bats! Yippe!!!

sites <- read.csv("netting_sites_2025.csv")

#make a interactive map

sites$Color <- c("hotpink", "darkblue", "purple", "green", "yellow", "brown", "orange", "black") 

leaflet(data = sites) %>%
   addProviderTiles(providers$OpenStreetMap, group = "Satellite") %>%
   addProviderTiles(providers$Esri.WorldImagery, group = "Road Map") %>%
   addProviderTiles(providers$Esri.WorldTopoMap, group = "Terrain") %>%
  addCircleMarkers(
    lng = ~ Longitude,
    lat = ~Latitude,
    color = ~Color,
    fillColor =~Color,
    fillOpacity =~ 0.8,
    popup = ~paste("Latitude:", Latitude, "<br>Longitude:", Longitude)
)%>%
    fitBounds(~min(Longitude), ~min(Latitude), ~max(Longitude), ~max(Latitude)) %>%
    addLayersControl(
    baseGroups = c("Road Map", "Satellite", "Terrain"),
    options = layersControlOptions(collapsed = FALSE)
)%>%
    addScaleBar(position = "bottomleft") %>%
    addLayersControl(
    baseGroups = c("Road Map", "Satellite", "Terrain"),
    options = layersControlOptions(collapsed = FALSE)
)

The red outline indicates the entirety of Fort Campbell. The sites are differentiated by color.

library(knitr)
## 
## Attaching package: 'knitr'
## The following object is masked from 'package:terra':
## 
##     spin
kable(sites[, c("Sites", "Latitude", "Longitude", "Color")],
      col.names = c("Site", "Latitude", "Longitude", "Color"),
)
Site Latitude Longitude Color
23 36.61840 -87.61080 hotpink
109 36.58150 -87.63680 darkblue
324 36.56588 -87.51125 purple
113 36.60190 -87.56640 green
4 36.57450 -87.51200 yellow
6 36.65740 -87.77370 brown
116 36.62240 -87.77920 orange
2 36.66480 -87.78880 black 
sites <- read.csv("netting_sites_2025.csv")

#make images for markers
sites_markers <- 
  c("red_01.PNG",
    "red_02.PNG",
    "red_03.PNG",
    "red_04.PNG",
    "red_05.PNG",
    "red_06.PNG",
    "red_07.PNG",
    "red_08.PNG"
    )

site_icon <- makeIcon(sites_markers, iconWidth = 35, iconHeight = 35)
#make a interactive map

sites$Color <- c("hotpink", "darkblue", "purple", "green", "yellow", "brown", "orange", "black") 

leaflet(data = sites) %>%
   addProviderTiles(providers$OpenStreetMap, group = "Satellite") %>%
   addProviderTiles(providers$Esri.WorldImagery, group = "Road Map") %>%
   addProviderTiles(providers$Esri.WorldTopoMap, group = "Terrain") %>%
  addMarkers(
    lng = ~ Longitude,
    lat = ~Latitude,
    icon = site_icon,
    popup = ~paste("Latitude:", Latitude, "<br>Longitude:", Longitude)
) %>%
    fitBounds(~min(Longitude), ~min(Latitude), ~max(Longitude), ~max(Latitude)) %>%
    addLayersControl(
    baseGroups = c("Satellite"),
    options = layersControlOptions(collapsed = FALSE)
)%>%
    fitBounds(~min(Longitude), ~min(Latitude), ~max(Longitude), ~max(Latitude)) %>%
    addLayersControl(
    baseGroups = c("Road Map", "Satellite", "Terrain"),
    options = layersControlOptions(collapsed = FALSE)
)%>%
    addScaleBar(position = "bottomleft") %>%
    addLayersControl(
    baseGroups = c("Road Map", "Satellite", "Terrain"),
    options = layersControlOptions(collapsed = FALSE)
)